Efficient operation of solar photovoltaic (PV) systems is critical for maximizing power generation and ensuring optimal energy conversion. However, faults in PV modules can significantly impact system performance and reduce energy output. Therefore, accurate identification and diagnosis of these malfunctions are essential. To address the challenge of fault detection in solar PV systems, this study presents two distinct approaches. The first, called Solar Panel Degradation Assessment (SPDA), evaluates faults in solar panels by analyzing degradation effects while considering environmental factors like radiation and temperature. The second approach, named Efficient Ensemble Deep Learning Model for Enhancing Fault Detection in Solar PV Systems (AIFD-SolDL), utilizes advanced deep learning techniques, including DenseNet201, Inception-ResNet-v2, and Inception-v3, to enhance fault detection accuracy. In the AIFD-SolDL approach, PV module data undergo deep feature extraction followed by dimensionality reduction using principal component analysis (PCA). The reduced feature set is then used to train classifiers such as support vector machines (SVM), Gaussian Naive Bayes (GaussianNB), and random forests (RF) to differentiate between normal and fault conditions. Performance metrics, including precision, accuracy, recall, and F1-score, are computed for each combination of feature extractor and classifier. Extensive experiments with both the Solar Panel Images Dataset and the Infrared Solar Module Dataset show that the proposed approaches outperform state-of-the-art methods. For instance, the AIFD-SolDL approach utilizing SVM achieved perfect accuracy, precision, recall, and F1-score of 100% on the Solar Panel Images Dataset. Overall, the SPDA approach effectively detects faults, while deep learning techniques demonstrate high accuracy in fault classification, thereby enhancing the reliability of PV system maintenance and optimization.

This paper addresses an effective, reliable and fast charging method for maximizing lithium-ion battery performance, longevity, and safety. The proposed multi-stage current charging mechanism utilizes a modified multi-stepped constant current-constant voltage based on the particle swarm optimization (MMSCC-CV-PSO) algorithm. The proposed MMSCC-CV-PSO charging strategy demonstrates a significantly faster charging performance compared to traditional charging techniques, including the widely used constant-current constant-voltage (CC-CV) method and the conventional multistage constant current (MSCC) approach. This enhanced efficiency highlights the superiority of the proposed method over existing solutions discussed in the literature. The simulation results confirm the practicality and superior performance of the proposed design strategy. Compared to the conventional MSCC method, the proposed approach achieved a considerable reduction in battery charging time, indicating a more efficient energy transfer process. Additionally, it led to a noticeable decrease in heat generation within the battery during the charging cycle, reflecting improved thermal management. These outcomes collectively demonstrate the effectiveness of the proposed method in enhancing both the speed and safety of the charging process, making it a promising solution for advanced battery management systems. The simulation model was developed and successfully implemented using the MATLAB/Simulink environment, providing a flexible platform for testing and evaluating the proposed charging strategy.

Due to advancements in existing Internet of Medical Things (IoMT) systems and devices, the blood glucose level (BGL) for type-1 diabetic patients (T1DPs) is effectively and continually monitored and controlled by Artificial Pancreas. Because the regulation of BGL is a very complex process, many efforts have been conducted to design a powerful and effective controller for the exogenous insulin infusion system. The main objective of this study is to propose an optimized interval type-2 fuzzy (IT2F) based controller of artificial pancreas for regulation BGL of T1DP based on IoMT. The proposed controller should avoid the risk of hyperglycemia and hypoglycemia situations that T1DP faces during the infusion of exogenous insulin. The main contribution of this work is using meta-heuristic method called grey wolf optimizer (GWO) to tune the footprint of uncertainty for IT2F's membership functions to inject the proper dose of insulin under different conditions. The nonlinear extended Bergman minimal model (EBMM) with uncertainty is used to represent the blood glucose regulation and represent the dynamics of meal disturbance in T1DP. The effectiveness and the performance of the proposed controller are investigated using MATLAB/Simulink platform. Simulation results show that the proposed controller can avoid both severe hypoglycemia and hyperglycemia for nominal parameters of the model, in addition to model under the presence of both parametric uncertainty and uncertain meal disturbance.

To increase the efficiency of photovoltaic (PV) array output under variable environmental conditions, maximum power point tracking (MPPT) of the solar arrays is needed. This paper proposes fuzzy logic controller (FLC)-based MPPT, artificial neural network (ANN)-based MPPT, neuro-fuzzy (NF)-based MPPT, particle swarm optimisation (PSO)-based MPPT, and cuckoo search (CS) algorithm-based MPPT to combine an adaptive controller and an optimisation, to guarantee global stability and a constant settling time for all operation conditions. This combination enables an increase in the power generated in comparison with conventional MPPT techniques. Simulation results show that the proposed photovoltaic/storage generator is able to supply the suggested dynamic loads under different conditions, and achieve good performance. It is also noticed that operating the photovoltaic array based on maximum power point tracking conditions gives about 43% extra power generation than in the case of normal operation.

Recently, interval type-2 fuzzy logic controller (IT2FLC) which is considered a simple class of the general Type-2 fuzzy logic controller (T2FLC) has shown superiority in different applications in dealing with uncertainties and minimizing its impact on the system, in fact superiority of IT2FLC over ordinary Type-1 fuzzy logic controller (T1FLC) in decreasing the effect of uncertainties in the system comes through the best choice of the unique property of IT2FLC called footprint of uncertainty (FOU) which is not included in T1FLC. Varying FOU can save a more degree of freedom in designing IT2FLC. It can be chosen using an appropriate optimization method where the selection of proper optimization method will develop the effectiveness of IT2FLC and increase its ability to minimize uncertainties in the system. Based on the above, an IT2FLC is utilized in this study with its parameters are tuned using different Bio-inspired optimization techniques to determine the effect of tuning FOU that achieves a good system performance. A comparison includes these optimization techniques for IT2FLC such as ant lion optimization (ALO), grey wolf optimization (GWO), artificial hummingbird algorithm (AHA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and T1FLC method is constructed using some performance criteria to determine the proper approach that achieves satisfactory performance. The performance and the effectiveness of the proposed control approach were investigated in Matlab/Simulink platform. Simulation results showed that IT2FLC based on bio-inspired optimization methods always outperforms T1FLC under different scenarios where it shows high stability, and it can dampen out the effect of uncertainties in the system.

Regulating blood glucose level (BGL) for type-1 diabetic patient (T1DP) accurately is very important issue, an uncontrolled BGL outside the standard safe range between 70 and 180 mg/dl results in dire consequences for health and can significantly increase the chance of death. So the purpose of this study is to design an optimized controller that infuses appropriate amounts of exogenous insulin into the blood stream of T1DP proportional to the amount of obtained glucose from food. The nonlinear extended Bergman minimal model is used to present glucose-insulin physiological system, an interval type-2 fuzzy logic controller (IT2FLC) is utilized to infuse the proper amount of exogenous insulin. Superiority of IT2FLC in minimizing the effect of uncertainties in the system depends primarily on the best choice of footprint of uncertainty (FOU) of IT2FLC. So a comparison includes four different optimization methods for tuning FOU including hybrid grey wolf optimizer-cuckoo search (GWOCS) and fuzzy logic controller (FLC) method is constructed to select the best controller approach. The effectiveness of the proposed controller was evaluated under six different scenarios of T1DP using Matlab/Simulink platform. A 24-h scenario close to real for 100 virtual T1DPs subjected to parametric uncertainty, uncertain meal disturbance and random initial condition showed that IT2FLC accurately regulate BGL for all T1DPs within the standard safe range. The results indicated that IT2FLC using GWOCS can prevent side effect of treatment with blood-sugar-lowering medication. Also stability analysis for the system indicated that the system operates within the stability region of nonlinear system.

Chaos study and control is one of the dynamic fields of research nowadays. The non-linear dynamic behavior such as fast scale bifurcation results in an unpredictable chaos action so it is essential to eliminate this behavior. So, the main objective of this paper is to propose an optimized fuzzy fractional PID controller to suppress the chaos action and stabilize a nonlinear chaotic. The cuckoo search optimization (CSO) technique’s optimal solution is used to optimize the fractional order controller parameters while considering integral time absolute error as the objective function and the FLC will adapt the controller parameters so that it provides a good performance. Here, the boost converter for permanent magnet DC motor is selected as a chaotic system as case study. The dynamic characteristic of the system is analysed by waveform, phase portrait, and Fillipov theory. Then, the proposed controller is compared with three additional controllers, such as controlling saltation matrix, optimized-PID, and optimized-Fractional PID. The proposed technique enhances the performance of the system and stabilized the response of the output and suppress the bifurcation behavior for nonlinear system. Finally, the effectiveness of the proposed technique has justified by simulation results on the PMDC drive system in a specified interval with variation the load to emphasize its superiority over all other controllers.

Photovoltaic panels (PVs) are solar panels that turn sunlight into electricity. Tracking the maximum power point (MPP) of PVs is especially important for economic issues. The most popular maximum power point tracking techniques are perturb and observation, hill climbing, constant voltage, parasitic capacitance, and incremental conductance (INC). However, these techniques give oscillated results about the MPP that causes low accuracy, especially in partial shading conditions. This paper is discussing the enhancement of photovoltaic energy system performance using several metaheuristic optimization algorithms. Using MATLAB SIMULINK, a comparative analysis of several algorithms for tracking MPP of PV systems under partially shadowed conditions was conducted. The metaheuristic optimization algorithms that are used in this paper are particle swarm optimization (PSO), cuckoo search algorithm (CSA), grey wolf optimization (GWO), and whale optimization algorithm (WOA). The results show that using WOA and GWO achieved the best efficiency in tracking MPP, whereas, using PSO and CSA achieved lower efficiency in tracking MPP. The MPP of the PV system was not tracked by INC under the partial shaded conditions.

This paper addresses a developed viable fault-detection scheme based on power evaluation and observation against partial shading degradation faults in solar photovoltaic (PV) system. To extract maximum power from photovoltaic systems and increase their efficiency and performance under atmospheric circumstances changes that leads to PV system failures, like solar irradiance and ambient temperature. An explicit fault detection algorithm is derived to detect the effectiveness factor as well as the threshold indicator relating the partial shading faults in PV array. Simulation result on PV array is demonstrated to justify the validation of the proposed design scheme. The faulty part of the array is detected with a short and acceptable time period and a computation load reduction for large number of PV modules.

This article addresses a hybrid pseudo-PD/machine learning controller algorithm to improve the stabilization and trajectory tracking of the ball on the plate system (BOPS). The proposed controller design depends on a machine learning algorithm that detect the angle of the servo motor required to correct the position of the ball on the plate also the parameters of the PD controller is changed online using the fuzzy logic to enhance the performance of the trajectory and point tracking of the system. This article offers three different machine learning techniques for predicting servo motor angle that obtain higher accuracies of 99.95%, 99.908%, and 99.998% for support vector regression, decision tree regression, and random forest regression, respectively. The proposed scheme has greatly improved the system's settling time and overshoot, according to simulation and practical results. The Lagrangian formulation may be used to obtain the mathematical formulation, and a practical identification experiment can be used to determine the servo motor parameter. Simulation and practical results on a BOPS dynamic model are both demonstrated to justify the validation of the proposed design scheme. The experimental practical results justify the feasibility and effectiveness of the proposed controller design strategy.

- A precision model of a battery charge equalization controller (BCEC) is developed in this research paper to control a series-connected Li-ion battery with a number of cells (n). The BCEC's main task is to manage each cell individually by monitoring and balancing all cells by charging the over-discharged cell or discharging the overcharged one. An intelligent fuzzy logic controller (FLC) and a single sliding mode controller (SSMC) are evolved to activate bidirectional cell switches and regulate a chopper circuit's direct current (DC-DC flyback converter) with PWM generation. The model can be implemented in electric vehicle (E.V.) applications to get benefit from the Li-ion battery. It consists of individual models of an E.V., cells of Li-ion battery, a fly-back converter, and a charge equalization controller for charging and discharging are integrated with n series-connected cells of Li-ion battery. The simulation results confirm that the proposed schemes have achieved enhanced performance with balancing the Li-ion cells as the state of charge (SOC) difference between cells is maintained to be 0.1% while maintaining the battery operation with a safe region. The BCEC is compared with the existing controllers based on efficiency, power losses, performance, and cost and has achieved better results.

A fractional-order model of a photovoltaic (PV) system is proposed in this paper. The system identification approach is used to develop an effective dynamical model for a PV system. A real PV module and a boost converter are used to gather the experimental input–output data for the identification process. The black box modeling is applied to the system identification to obtain the transfer function, without the requirement to perform any mathematical analysis. The identification process is based on the least squares criteria for minimizing model output error, and the Levenberg–Marquardt algorithm is used for optimizing the model parameters. The proposed fractional-order model (FOM) is investigated using MATLAB to study the frequency response and the model's stability. Simulation results verify the effectiveness and advantages of the FOM in comparison to identified integer-order model.

Increasing the efficiency of photovoltaic (PV) systems is a pressing issue, and several studies have focused on the Maximum Power Point Tracking (MPPT) techniques to extract the maximum PV output power. Many MPPT techniques have been discussed in the last decade, and optimization-based MPPT techniques have shown better performance than other MPPT techniques. In this study, two optimization techniques, the cuckoo search algorithm and particle swa9rm optimization with changing inertia weight techniques are discussed and applied to a PV system to track the maximum power point. The MSX-60 PV module and boost DC-DC converter are used in this paper to simulate and model the MPPT system using MATLAB/Simulink to show which technique has the best performance under various solar irradiation scenarios. In addition, different structures of PV arrays such as series-parallel, bridge link, and total cross-tied PV structures are simulated to analyze their effect on the efficiency of MPPT processes.

This paper discusses an efficient method to improve the balancing and tracking of the trajectory of the BOPS based on machine learning (ML) algorithm with the Pseudo proportional-derivative (PPD) controller. The proposed controller depends on a ML technique that detect the angle of the servo motor required to correct the ball position on the plate. This paper presents three different ML algorithms for the servo motor angle prediction and achieved higher accuracy which are 99.855%, 99.999%, and 99.998% for support vector regression, decision tree regression, and random forest regression, respectively. The simulation results demonstrate that the proposed strategy has significantly improved the settling time and overshoot of the system. The mathematical formulation can be obtained using the Lagrangian formulation and the servo motor parameter obtained by a practical identification experiment.

In this paper, the study aims to evolve a precision model of a Battery charge equalization controller (BCEC) that manage each cell of a lithium-ion (Li-ion) battery, monitoring a nd balancing by charging a nd discharging a series-connected Li-ion battery with several cells (n) in electric vehicle (E.V.) applications. An intelligent Sliding mode controller is evolved to activate bidirectional cell switches and regulate a chopper circuit’s direct current (DC-DC flyback converter circuit) with PWM generation. To implement a small-scale BCEC, the model consists of individual models of an E.V., cells of Li-ion battery, a fly-back converter, and a single sliding mode controller (SSMC) for charging and discharging are integrated with n series-connected cells of Li-ion-battery. The BCEC output shows that the cells are operated within a safe range, and the difference in the state of charge (SOC) is maintained to be 0.1%. Comparing the developed BCEC with the existing controllers, with the performance, control algorithm, efficiency, power loss, size, and cost.

After applying the ball on plate stabilization system in practice, a big problem appeared, which is that the servo motors used must be very fast and have large-angle but these motors are rare and expensive Therefore, commonly used servo motors that have large angles and low speed are used in the practical experiment, and this makes the angle of the plate large and this increases the nonlinearity in this system, Therefore, a model was developed that deals with the large nonlinearity in the system, depending on Takagi-Sugeno (T-S) fuzzy to improve the tracking and stabilization of the ball on the plate system (BOPS) and this model not presented in any scientific paper before. This paper introduces the mathematical model of the two degree of freedom BOPS using state space and modeling through Takagi-Sugeno (T-S) fuzzy. The T-S fuzzy is utilized for improving the controller performance by aiding the state feedback controller. The determination of the gains of the controller is performed through the linear quadratic regulator (LQR) method. The proposed controller gives more precisely tracking the desired trajectory of the ball position.

This paper addresses a viable single loop PID controller on the bases of optimization algorithms for parallelly connected DC-DC converters to improve current sharing, improve the systems dynamics and guarantee good steady-state performance simultaneously. Because of inconvenience and lack of accuracy of Ziegler-Nichols rule in tuning PID controller parameters, an optimized controller design strategy with the purpose of enhancing the system performance is introduced in this paper. The PID is tuned by the traditional Ziegler -Nichols technique along with three other different algorithms: Genetic algorithm, whale algorithm and grey wolf algorithm. A comparison has been established between these algorithms based on the objective function value, execution time, overshoot, settling time and current sharing. The simulation results were collected to authenticate effectiveness of the proposed techniques and to evaluate the advantages of these optimization algorithms over the traditional tuning method.